Literature DB >> 12419599

How best to geo-reference farms? A case study from Cornwall, England.

P A Durr1, A E A Froggatt.   

Abstract

The commonest way of geo-referencing farms as single points is using the location of the farmhouse as either read off a map or approximated by its postcode. While these two methods may be adequate for small farms, they are unlikely to be satisfactory for large ones, or alternatively when they are comprised of several discrete units or holdings. In order to investigate the best representation of the total farm polygon(s) by a single point, we undertook a study using nearly 500 actual farm boundaries in the county of Cornwall, England. For each farm, the farm boundaries were digitised, and its area and centroid determined using ArcView 3.2. A variety of point geo-referencing systems were tested to find the best single point location for a farm, as judged by the proportion of farm area captured. Whilst the centroid was found to capture the largest area, the main farm building was judged to be the best geo-referencing method for practical purposes. In contrast, the various systems of geo-coding using the farm postal address performed relatively poorly. Where there are separate parcels of land managed together in a single parish, they may be identified as a single unit, but if there are separate parcels in different parishes they should be identified as separate units.The implications of these results for Great Britain's national animal health information system (VETNET) are discussed.

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Year:  2002        PMID: 12419599     DOI: 10.1016/s0167-5877(02)00123-x

Source DB:  PubMed          Journal:  Prev Vet Med        ISSN: 0167-5877            Impact factor:   2.670


  7 in total

1.  The effect of administrative boundaries and geocoding error on cancer rates in California.

Authors:  Daniel W Goldberg; Myles G Cockburn
Journal:  Spat Spatiotemporal Epidemiol       Date:  2012-02-10

2.  Risk mapping of Rinderpest sero-prevalence in Central and Southern Somalia based on spatial and network risk factors.

Authors:  Angel Ortiz-Pelaez; Dirk U Pfeiffer; Stefano Tempia; F Tom Otieno; Hussein H Aden; Riccardo Costagli
Journal:  BMC Vet Res       Date:  2010-04-28       Impact factor: 2.741

3.  Spatial distribution of the active surveillance of sheep scrapie in Great Britain: an exploratory analysis.

Authors:  Colin P D Birch; Ambrose C Chikukwa; Kieran Hyder; Victor J Del Rio Vilas
Journal:  BMC Vet Res       Date:  2009-07-16       Impact factor: 2.741

4.  Classical sheep scrapie in Great Britain: spatial analysis and identification of environmental and farm-related risk factors.

Authors:  Kim B Stevens; Victor J Del Río Vilas; Javier Guitián
Journal:  BMC Vet Res       Date:  2009-09-08       Impact factor: 2.741

5.  Future Risk of Bovine Tuberculosis (Mycobacterium bovis) Breakdown in Cattle Herds 2013-2018: A Dominance Analysis Approach.

Authors:  Andrew W Byrne; Damien Barrett; Philip Breslin; Jamie M Madden; James O'Keeffe; Eoin Ryan
Journal:  Microorganisms       Date:  2021-05-06

6.  Geographic and topographic determinants of local FMD transmission applied to the 2001 UK FMD epidemic.

Authors:  Paul R Bessell; Darren J Shaw; Nicholas J Savill; Mark E J Woolhouse
Journal:  BMC Vet Res       Date:  2008-10-03       Impact factor: 2.741

7.  Liver fluke (Fasciola hepatica) infection in cattle in Northern Ireland: a large-scale epidemiological investigation utilising surveillance data.

Authors:  Andrew W Byrne; Stewart McBride; Angela Lahuerta-Marin; Maria Guelbenzu; Jim McNair; Robin A Skuce; Stanley W J McDowell
Journal:  Parasit Vectors       Date:  2016-04-14       Impact factor: 3.876

  7 in total

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